1. Field of the Invention
The present invention relates to a method of extracting and providing information about a natural language query, and more particularly, to a method and system for efficiently providing information related to a search result and a query entity, which match a user query intention, with respect to a sense-tagged natural language query having a simple or complex sentence structure.
2. Description of the Related Art
Ontology defines semantic relations between real-world objects such that a computer provides semantic specifications about information for accessing information inferred from complex relations between the real-world objects, and may be applied to various fields, such as artificial intelligence, information retrieval, and electronic commerce.
In detail, ontology expresses a variety of information on the web in semantic relations between targets, and thus logical query results that were unable to be found via general keyword-based searches in information retrieval fields may be obtained. For example, by using ontology, a result of a query, such as “what is the capital of a country that has the population of at least seventy million and is adjacent to the sea?”, may be obtained.
Such ontology information retrieval is effective in three aspects compared to keyword-based searches. First, the keyword-based searches provide only documents including an input keyword as search results, but the ontology information retrieval may directly provide a certain target a user wishes to find and attribute information about the certain target. Second, the keyword-based searches do not guarantee a search result that reflects relations between input keywords, but the ontology information retrieval may find information based on relations with other targets characterizing a target to be found. Third, the keyword-based searches only search preexisting information, but the ontology information retrieval may infer new information by finding a new relation between targets.
The ontology information retrieval largely helps to improve user convenience by immediately finding a query target desired by a user, but since an ontology query language that is not familiar to general users has to be used, the ontology information retrieval may be difficult to be approached by the general users. In other words, for the general users to access information built according to ontology, the general users have to learn a structure of ontology or an ontology query language, and thus utility of ontology may be low.
Meanwhile, a natural language is sufficient to express a logical meaning expressed in an ontology query language and is familiar to general users. Accordingly, if ontology information is accessible via a natural language query, the general users may easily access the ontology information, and thus utility of the ontology information retrieval may increase.
In order to access ontology information via a natural language query, a technology of converting the natural language query to an ontology query language is required. Accordingly, first, the structure and the meaning of the natural language query are analyzed by using a natural language processing technology; objects and relations of ontology, which correspond to natural language expressions, are found based on the analyzed structure and meaning; and the natural language query is converted to the ontology query language according to a grammar structure of the ontology query language.
However, in this case, there may be an inaccessible ontology query language due to errors in analyzing the structure and the meaning of the natural language query. In detail, there is no guarantee that a general natural language processing technology has all language resources required to process natural language expressions accessible to search target ontology, and since a minor grammatical error may produce a wrong analysis result, an inaccessible ontology query language may be generated.
Thus, according to a general search method, a user has to learn query types processable by a system through several trials and errors regarding a query having an error. In this case, a user who uses ontology-based information retrieval for the first time may not know which query is useful since the user is not aware of a query type accepted by a natural language processing technology and details about information built according to ontology.
Furthermore, according to such a general search method, a service provider is unable to provide all ontology information and sufficiently show utility of information owned by the service provider.
Most users wish to use ontology prepared by a service provider intuitively like keyword-based searches rather than meticulously learning information about the ontology or a query input method, and utilization and satisfaction on a new system are low compared to time and efforts invested to adapt to the new system.
Accordingly, studies on natural language understanding (NLU) are being conducted to increase utility and accuracy of information retrieval. NLU is used to determine an intention of a query of a user, wherein a query spoken or input in text by the user is an input and a list obtained by determining the intention of the query via NLU is an output of the query.
A sense-tagged natural language query may be complex compared to a general keyword-based query, but it is difficult to accurately determine the intention of the user and provide a search result matching the intention.